The goal of this analyzis is to understand how happy I am when I'm a Lithuanian and which criteria influence happiness equal the most
Data file 'World_Happiness_Report_2022.csv' contains information of 146 countries and the criteria by which it was assessed countries happiness rank
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import classification_report, confusion_matrix
import statsmodels.formula.api as smf
import statsmodels.api as sm
import random
from scipy import stats
df = pd.read_csv('World_Happiness_Report_2022.csv')
df.isnull().sum()
RANK 0 Country 0 Happiness score 0 Whisker-high 0 Whisker-low 0 Dystopia (1.83) + residual 0 Explained by: GDP per capita 0 Explained by: Social support 0 Explained by: Healthy life expectancy 0 Explained by: Freedom to make life choices 0 Explained by: Generosity 0 Explained by: Perceptions of corruption 0 dtype: int64
df = df.rename(columns={'Explained by: Social support': 'Social support',
'Explained by: GDP per capita': 'GDP per capita',
'Explained by: Healthy life expectancy' : 'Healthy life expectancy',
'Explained by: Freedom to make life choices' : 'Freedom to make life choices' ,
'Explained by: Generosity' : 'Generosity' ,
'Explained by: Perceptions of corruption' : 'Perceptions of corruption' })
df.head()
| RANK | Country | Happiness score | Whisker-high | Whisker-low | Dystopia (1.83) + residual | GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Finland | 7.821 | 7.886 | 7.756 | 2.518 | 1.892 | 1.258 | 0.775 | 0.736 | 0.109 | 0.534 |
| 1 | 2 | Denmark | 7.636 | 7.710 | 7.563 | 2.226 | 1.953 | 1.243 | 0.777 | 0.719 | 0.188 | 0.532 |
| 2 | 3 | Iceland | 7.557 | 7.651 | 7.464 | 2.320 | 1.936 | 1.320 | 0.803 | 0.718 | 0.270 | 0.191 |
| 3 | 4 | Switzerland | 7.512 | 7.586 | 7.437 | 2.153 | 2.026 | 1.226 | 0.822 | 0.677 | 0.147 | 0.461 |
| 4 | 5 | Netherlands | 7.415 | 7.471 | 7.359 | 2.137 | 1.945 | 1.206 | 0.787 | 0.651 | 0.271 | 0.419 |
len(df)
146
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 146 entries, 0 to 145 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 RANK 146 non-null int64 1 Country 146 non-null object 2 Happiness score 146 non-null float64 3 Whisker-high 146 non-null float64 4 Whisker-low 146 non-null float64 5 Dystopia (1.83) + residual 146 non-null float64 6 GDP per capita 146 non-null float64 7 Social support 146 non-null float64 8 Healthy life expectancy 146 non-null float64 9 Freedom to make life choices 146 non-null float64 10 Generosity 146 non-null float64 11 Perceptions of corruption 146 non-null float64 dtypes: float64(10), int64(1), object(1) memory usage: 13.8+ KB
df.describe().round(2)
| RANK | Happiness score | Whisker-high | Whisker-low | Dystopia (1.83) + residual | GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 | 146.00 |
| mean | 73.50 | 5.55 | 5.67 | 5.43 | 1.83 | 1.41 | 0.91 | 0.59 | 0.52 | 0.15 | 0.15 |
| std | 42.29 | 1.09 | 1.07 | 1.11 | 0.53 | 0.42 | 0.28 | 0.18 | 0.15 | 0.08 | 0.13 |
| min | 1.00 | 2.40 | 2.47 | 2.34 | 0.19 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 37.25 | 4.89 | 5.01 | 4.75 | 1.56 | 1.10 | 0.73 | 0.46 | 0.44 | 0.09 | 0.07 |
| 50% | 73.50 | 5.57 | 5.68 | 5.45 | 1.89 | 1.45 | 0.96 | 0.62 | 0.54 | 0.13 | 0.12 |
| 75% | 109.75 | 6.30 | 6.45 | 6.19 | 2.15 | 1.78 | 1.11 | 0.72 | 0.63 | 0.20 | 0.20 |
| max | 146.00 | 7.82 | 7.89 | 7.76 | 2.84 | 2.21 | 1.32 | 0.94 | 0.74 | 0.47 | 0.59 |
df['Country']= df['Country'].replace('*', '')
Happiness index variables into: 0 - Unhappy 1 - A little happy 2 - Happy 3 - Very happy 4 - In euphoria
def happiness_index(num):
if num > 8 :
return '4'
elif num > 6 :
return '3'
elif num > 4 :
return '2'
elif num > 2 :
return '1'
else:
return '0'
df['Happiness index'] = df['Happiness score'].map(happiness_index)
df[df['Country'] == 'Lithuania']
| RANK | Country | Happiness score | Whisker-high | Whisker-low | Dystopia (1.83) + residual | GDP per capita | Social support | Healthy life expectancy | Freedom to make life choices | Generosity | Perceptions of corruption | Happiness index | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | 34 | Lithuania | 6.446 | 6.535 | 6.356 | 2.153 | 1.804 | 1.204 | 0.659 | 0.496 | 0.053 | 0.077 | 3 |
Sort by continent
pip install pycountry-convert
Requirement already satisfied: pycountry-convert in c:\users\admin\anaconda3\lib\site-packages (0.7.2) Requirement already satisfied: pprintpp>=0.3.0 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (0.4.0) Requirement already satisfied: pytest-cov>=2.5.1 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (3.0.0) Requirement already satisfied: wheel>=0.30.0 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (0.37.0) Requirement already satisfied: pytest-mock>=1.6.3 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (3.7.0) Requirement already satisfied: repoze.lru>=0.7 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (0.7) Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: pycountry>=16.11.27.1 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (22.3.5) Requirement already satisfied: pytest>=3.4.0 in c:\users\admin\anaconda3\lib\site-packages (from pycountry-convert) (6.2.4) Requirement already satisfied: setuptools in c:\users\admin\anaconda3\lib\site-packages (from pycountry>=16.11.27.1->pycountry-convert) (58.0.4) Requirement already satisfied: attrs>=19.2.0 in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (21.2.0) Requirement already satisfied: iniconfig in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (1.1.1) Requirement already satisfied: packaging in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (21.0) Requirement already satisfied: pluggy<1.0.0a1,>=0.12 in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (0.13.1) Requirement already satisfied: py>=1.8.2 in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (1.10.0) Requirement already satisfied: toml in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (0.10.2) Requirement already satisfied: atomicwrites>=1.0 in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (1.4.0) Requirement already satisfied: colorama in c:\users\admin\anaconda3\lib\site-packages (from pytest>=3.4.0->pycountry-convert) (0.4.4) Requirement already satisfied: coverage[toml]>=5.2.1 in c:\users\admin\anaconda3\lib\site-packages (from pytest-cov>=2.5.1->pycountry-convert) (6.3.2) Requirement already satisfied: tomli in c:\users\admin\anaconda3\lib\site-packages (from coverage[toml]>=5.2.1->pytest-cov>=2.5.1->pycountry-convert) (2.0.1) Requirement already satisfied: pyparsing>=2.0.2 in c:\users\admin\anaconda3\lib\site-packages (from packaging->pytest>=3.4.0->pycountry-convert) (3.0.4)
from pycountry_convert import country_alpha2_to_continent_code, country_name_to_country_alpha2
def get_continent(col):
try:
cn_a2_code = country_name_to_country_alpha2(col)
except:
cn_a2_code = 'Unknown'
try:
cn_continent = country_alpha2_to_continent_code(cn_a2_code)
except:
cn_continent = 'Unknown'
return ( cn_continent)
df['Continent'] = df['Country'].map(get_continent)
pip install plotly
Requirement already satisfied: plotly in c:\users\admin\anaconda3\lib\site-packages (5.7.0) Requirement already satisfied: six in c:\users\admin\anaconda3\lib\site-packages (from plotly) (1.16.0) Requirement already satisfied: tenacity>=6.2.0 in c:\users\admin\anaconda3\lib\site-packages (from plotly) (8.0.1) Note: you may need to restart the kernel to use updated packages.
import plotly.graph_objs as go
from plotly.offline import iplot
data = dict(type='choropleth',
locations=df['Country'],
locationmode='country names',
z=df['Happiness score'],
text=df['Country'],
colorbar={'title': 'Happiness'},
colorscale=[[0, 'rgb(31,125,180)'],
[0.1, 'rgb(51,160,44)'],
[0.50, 'rgb(255,255,20)'],
[1, 'rgb(210,26,28)']],
reversescale = False)
layout = dict(title='World Happiness Rankings 2022',
autosize=False,
width=800,
height=800,
geo=dict(showframe=True, projection={'type': 'van der grinten'}))
choromap = go.Figure(data=[data], layout=layout)
iplot(choromap, validate=False)
TOP 3 continents by average of 'Happiness score'
df.groupby(df['Continent'])['Happiness score'].mean().sort_values(ascending=False)[0:3]
Continent OC 7.181000 EU 6.462108 NA 6.291500 Name: Happiness score, dtype: float64
Criteria according to which Lithuania lags behind the country with the highest rating
LT_compare = df[df['Country'].isin(["Lithuania","Finland"])]
LT_compare.iloc[0,6:12].plot(color = 'b', figsize = (11,4))
LT_compare.iloc[1,6:12].plot(color = 'g', figsize = (11,4))
plt.xticks(rotation=90)
labels=['Finland', 'Lithuania']
plt.legend(labels)
plt.show()
It shows that Lithuania lagging behind the most of 'Freedom to make life choices' and 'Perceptions of coruption'
sns.pairplot(df, hue='Happiness index' , palette='husl', vars=['GDP per capita',
'Social support',
'Healthy life expectancy',
'Freedom to make life choices',
'Generosity',
'Perceptions of corruption'])
<seaborn.axisgrid.PairGrid at 0x19430ae0610>
It shows that 'Generosity' and 'Perceptions of coruption' criteria has hop distributions between all happiness indexes and therefore I can assume that they are at least make influence
plt.subplots(figsize = (8,8))
sns.heatmap(df.corr(), annot=True)
plt.title("Correlations Among Features", fontsize = 20);
It shows that countries with higher GDP ,their people has higher healthy life expectancy and social support
Relationship Between GDP and healthy life expectancy
x=df['Healthy life expectancy']
y=df['GDP per capita']
parameter = np.polyfit(x,y,1)
y2 = parameter[0] * x + parameter[1]
plt.figure(figsize=(8,6))
sns.scatterplot(x=x,y=y)
plt.plot(x, y2, color='r')
plt.show()
plt.subplots(figsize = (6,2))
sns.barplot(y='Country', x='GDP per capita' , data=df.nlargest(5, 'GDP per capita'), palette='magma')
plt.xticks()
plt.xlabel('GDP per capita')
plt.ylabel('Country')
plt.title('Top 5 Counties with highest GDP per capita')
plt.show()
plt.subplots(figsize = (7,2))
sns.barplot(x ='Happiness score', y = 'Country', data=df.nlargest(5, 'Happiness score'), palette='magma')
plt.xticks()
plt.ylabel("Country")
plt.xlabel("Happiness score")
plt.title("Top 5 Counties with highest happiness score", fontsize = 10)
plt.show()
It shows that GDP per capita is not the most significant criterion
df.columns
Index(['RANK', 'Country', 'Happiness score', 'Whisker-high', 'Whisker-low',
'Dystopia (1.83) + residual', 'GDP per capita', 'Social support',
'Healthy life expectancy', 'Freedom to make life choices', 'Generosity',
'Perceptions of corruption', 'Happiness index', 'Continent'],
dtype='object')
y = df['Happiness score']
x = df[['GDP per capita', 'Social support','Healthy life expectancy', 'Freedom to make life choices',
'Generosity','Perceptions of corruption']]
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
len(x_train)
116
lm = LinearRegression()
lm.fit(x_train, y_train)
LinearRegression()
lm.intercept_
1.6557713368895577
coeff_df = pd.DataFrame(lm.coef_, x.columns, columns=['Coefficients'])
coeff_df.round(2)
| Coefficients | |
|---|---|
| GDP per capita | 0.47 |
| Social support | 1.60 |
| Healthy life expectancy | 1.39 |
| Freedom to make life choices | 1.43 |
| Generosity | 1.07 |
| Perceptions of corruption | 0.31 |
It shows that the most significant criteria is 'Social support' and 'Freedom to make life choices'
predictions = lm.predict(x_test)
plt.scatter(x=predictions, y=y_test)
<matplotlib.collections.PathCollection at 0x194336ab3d0>
from sklearn.metrics import r2_score
import statsmodels.formula.api as smf
r2_score(y_test, predictions)
0.8436702795126736
sns.lmplot(x='Happiness score', y='Social support', data=df)
<seaborn.axisgrid.FacetGrid at 0x1943394a220>
sns.lmplot(x='Happiness score', y='Freedom to make life choices', data=df)
<seaborn.axisgrid.FacetGrid at 0x194336f1310>
